Primary potential noise sources are device power interference, baseline drift, muscle noise, electrode contact noise. Among these motion artifacts, caused by muscle movements, can be mistakenly registered as arrhythmia [1]}. Therefore, proper pre-processing is an essential step before analyzing the ECG signal. Several approaches are proposed to denoise ECG signals, the majority of them are traditional methods that are based on parameters that are highly vulnerable to noise, such as fixed filters like finite impulse response filters (FIR)[2]} and infinite impulse response filters(IIR)[3]}. Wavelet methods[4]}[5]}[6]}, adaptive filtering [7]}[8]}, and empirical mode decomposition (EMD) [9]}[10]} are the other set of methods which requires high computational resources. In the modern era, mobile-based ECG devices and patches are introduced that require less complex and high yield algorithms. One such method for filtering ECG is Denoising Autoencoders (DAE) that has shown better filtering capabilities than other techniques because of their powerful nonlinear mapping capabilities.[11]} proposed fully convolution network (FCN)[12]} based DAE for signal denoising. FCN network used is of 13 layers \(SNR_imp\)  achieved was 15.49db on MIT-BIH database signal, which was corrupted by noise from MIT-BIH-NST database [13]}.
